system

Abstract

The system according to the embodiment aims to make optimal suggestions based on personal data. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, and a proposal unit. The acquisition unit registers user data on the Cloud. The analysis unit analyzes the data collected by the acquisition unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide optimal suggestions based on personal data, and there is room for improvement.

[0005] The system according to the embodiment aims to make optimal suggestions based on personal data. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a proposal unit. The acquisition unit registers user data on the cloud. The analysis unit analyzes the data collected by the acquisition unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can make optimal suggestions based on personal data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A recommendation system according to an embodiment of the present invention registers personal data on the cloud and analyzes it with a generation AI. The system then makes optimal recommendations for clothing, glasses, accessories, and shoes based on data such as face, body type, orientation, and chronic illnesses. In this recommendation system, users register their own face photos, body type information, orientation, chronic illnesses, and other data on the cloud. The generation AI then analyzes this data and recommends items that are optimal for the user. For example, the system can recommend glasses that fit their face shape, clothes that fit their body type, and shoes that take chronic illnesses into consideration. This system allows users to easily find the items that are best for them, improving shopping efficiency. First, a user registers their own data on the cloud. At this time, they input detailed data such as a face photo, body type information, orientation, and chronic illnesses. For example, they upload a face photo and enter their height, weight, preferred style, allergy information, and so on. Next, the generation AI analyzes the input data. The generation AI determines the face shape from the face photo and the body type from the body type information. Furthermore, the system considers the orientation and chronic illness data to recommend items that are optimal for the user. For example, the system can recommend glasses that fit their face shape, clothes that fit their body type, and shoes that take chronic illnesses into consideration. The generated suggestions are provided to the user. For example, the suggestions are displayed on the user's smartphone or computer. The user can review the suggested items and purchase them. This system allows users to easily find the items that best suit them, improving shopping efficiency. For example, by being recommended glasses that fit the shape of the face, users will no longer have to worry about choosing glasses. Also, by being recommended clothes that fit the body type, users can avoid the hassle of trying on clothes. Furthermore, by being recommended shoes that take into account chronic illnesses, users can enjoy fashion while maintaining their health. This allows the suggestion system to efficiently collect and analyze user data and make optimal suggestions.

[0029] The proposal system according to the embodiment includes an acquisition unit, an analysis unit, and a proposal unit. The acquisition unit registers user data on the cloud. The user data includes, but is not limited to, a face photo, body shape information, inclinations, and chronic illnesses. For example, the acquisition unit allows the user to upload a face photo and input information such as height, weight, preferred style, and allergy information. The acquisition unit can also encrypt and store the user data to ensure security. For example, the acquisition unit may perform an authentication process when registering data to protect the user data. The analysis unit uses a generation AI to analyze the data collected by the acquisition unit. For example, the generation AI may determine the face shape from the face photo and the body type from the body shape information. The generation AI may also suggest items that are optimal for the user, taking into account the inclinations and chronic illness data. For example, the generation AI may analyze the facial contours and suggest the optimal eyeglass frames for the user. The generation AI may also suggest the optimal clothing size and design for the user based on the body shape information. The generation AI may also suggest the optimal shoes for the user, taking into account chronic illness data. The suggestion unit suggests optimal items to the user based on the analysis results obtained by the analysis unit. The suggestion unit suggests, for example, glasses that fit the user's face shape, clothes that fit the user's body type, or shoes that take chronic illnesses into consideration. The suggestion unit can also display the suggestions on the user's smartphone or computer. For example, the suggestion unit sends a notification to the user's smartphone to display the suggestions. The suggestion unit can also display the suggestions on the user's computer so that the user can check the suggested items. This allows the suggestion system to efficiently collect and analyze user data and make optimal suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can display suggestions generated by the generation AI to the user.

[0030] The acquisition unit can register the user's facial photo or body shape information, orientation, and chronic illness data on the Cloud. For example, the acquisition unit allows the user to upload a facial photo and input information such as height, weight, preferred style, and allergy information. The acquisition unit can also encrypt and store the user's data to ensure security. For example, the acquisition unit can perform an authentication process when registering data to protect the user's data. This enables more accurate analysis and suggestions by registering detailed user data on the Cloud. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input a user's facial photo into the generation AI and have the generation AI analyze the facial photo.

[0031] The analysis unit can determine the face shape from a facial photograph and the body type from body type information. The analysis unit can determine the face shape from a facial photograph, for example, using a generation AI. For example, the generation AI can analyze the facial contours and suggest the most suitable eyeglass frames for the user. The analysis unit can also determine the body type from the body type information, for example, using the generation AI. For example, the generation AI can suggest the most suitable clothing size and design for the user based on the body type information. This makes it possible to perform analysis to suggest the most suitable items for the user based on the facial photograph and body type information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input a user's face photo into the generation AI and have the generation AI determine the face shape.

[0032] The analysis unit can suggest items to the user based on data on preferences or chronic illnesses. The analysis unit, for example, uses a generation AI to suggest items that are optimal for the user, taking into account data on preferences and chronic illnesses. For example, the generation AI can suggest optimal clothing and accessories based on the user's hobbies and preferences. The generation AI can also suggest shoes that take into account the user's chronic illnesses. This makes it possible to make suggestions that take into account the user's preferences and chronic illnesses. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's preference data into the generation AI and have the generation AI suggest optimal items.

[0033] The suggestion unit can suggest glasses that fit the shape of the face, clothes that fit the body type, or shoes that take chronic illnesses into consideration. The suggestion unit can suggest glasses that fit the shape of the face, for example, using a generation AI. For example, the generation AI can analyze the contours of the face and suggest the best eyeglass frames for the user. The suggestion unit can also suggest clothes that fit the body type, for example, using the generation AI. For example, the generation AI can suggest the best size and design of clothes for the user based on body type information. The suggestion unit can also suggest shoes that take chronic illnesses into consideration, for example, the generation AI can suggest the best shoes taking into account the user's chronic illness data. This improves shopping efficiency by suggesting the best items for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can display the suggestions generated by the generation AI to the user.

[0034] The suggestion unit can display the suggested content on the user's smartphone or computer. For example, the suggestion unit sends a notification to the user's smartphone and displays the suggested content. The suggestion unit also displays the suggested content on the user's computer, allowing the user to check the suggested items. This allows the user to check the suggested content and purchase them. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can display suggested content generated by a generation AI to the user.

[0035] The acquisition unit can analyze the user's past data registration history and select the optimal acquisition method. For example, the acquisition unit can prioritize and suggest data acquisition methods (e.g., voice input or manual input) that the user has frequently used in the past. The acquisition unit can also automatically select necessary data items based on the types of data the user has previously registered. Furthermore, the acquisition unit can also suggest the most efficient data acquisition method from the user's past data registration history. This enables efficient data acquisition by selecting the optimal data acquisition method based on the past data registration history. Some or all of the above-mentioned processing in the acquisition unit can be performed, for example, using a generation AI or without using a generation AI. For example, the acquisition unit can input the user's past data registration history into the generation AI and have the generation AI select the optimal acquisition method.

[0036] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring data. The acquisition unit selects an appropriate data acquisition timing based on, for example, the user's current living situation (e.g., whether the user is at work or on vacation). The acquisition unit can also prioritize acquisition of related data items based on the user's areas of interest (e.g., fashion or health). Furthermore, the acquisition unit can filter unnecessary data items based on the user's living situation and areas of interest to perform efficient data acquisition. This enables efficient data acquisition based on the user's living situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0037] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring data related to that area. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring data related to the travel destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring data related to the user's home. This allows highly relevant data to be acquired efficiently by taking the user's geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, or without, the generation AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant data.

[0038] The acquisition unit can analyze the user's social media activities and acquire related data when acquiring data. The acquisition unit can acquire related data based on, for example, information shared by the user on social media. The acquisition unit can also analyze the user's social media activity history and acquire data based on their interests. Furthermore, the acquisition unit can acquire related data based on information about accounts the user follows on social media. This allows for efficient acquisition of related data based on the user's social media activities. Some or all of the above-described processing in the acquisition unit can be performed using, or without, a generation AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a face recognition algorithm to face photo data. The analysis unit can also apply a body type analysis algorithm to body type information. Furthermore, the analysis unit can select an appropriate algorithm for data on orientation and chronic illnesses and perform analysis. This enables highly accurate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of data submission into the generation AI and have the generation AI determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of analysis.

[0043] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an item with a high level of importance. The suggestion unit can also make a concise suggestion for an item with a low level of importance. Furthermore, the suggestion unit can adjust the depth and scope of the suggestion based on the importance of the item. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the item. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the importance of the item to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0044] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the item. For example, for clothes, the suggestion unit can apply a suggestion algorithm based on fashion style. For eyeglasses, the suggestion unit can also apply a suggestion algorithm based on face shape. For shoes, the suggestion unit can also apply a suggestion algorithm based on chronic illnesses or foot shape. This enables highly accurate suggestions by applying a suggestion algorithm depending on the category of the item. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the category of the item into the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0045] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the item. For example, the proposal unit prioritizes the most recent item. The proposal unit can also postpone the proposal of an item that was submitted earlier. Furthermore, the proposal unit can adjust the order of proposals based on the submission time. This enables efficient proposals by determining the priority of proposals based on the submission time of the item. Some or all of the above-described processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the submission time of the item into the generation AI and have the generation AI determine the priority of the proposals.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant items. The suggestion unit can also postpone suggesting less relevant items. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the items. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the items. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the relevance of items to the generation AI and cause the generation AI to adjust the order of suggestions.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The recommendation system can further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit collects and analyzes data on items purchased in the past by the user. For example, the purchase history analysis unit can analyze the styles and brands of clothes and accessories purchased by the user in the past to understand the user's preferences. The purchase history analysis unit can also understand trends in items that the user frequently purchases and reflect this in the next recommendation. Furthermore, the purchase history analysis unit can analyze ratings and reviews of items purchased by the user in the past to extract characteristics of items that the user is satisfied with. This enables more accurate recommendations based on the user's purchase history.

[0049] The recommendation system may further include a lifestyle acquisition unit that collects lifestyle data of the user. The lifestyle acquisition unit collects and analyzes data related to the user's daily life. For example, the lifestyle acquisition unit may collect data on the user's exercise habits, diet, sleep patterns, etc. The lifestyle acquisition unit may also collect data on the user's hobbies and interests. The lifestyle acquisition unit may also collect data on the user's living environment and work situation. This makes it possible to make recommendations based on the user's lifestyle.

[0050] The proposal system may further include a health data acquisition unit that collects health data of the user. The health data acquisition unit collects and analyzes data related to the user's health condition. For example, the health data acquisition unit may collect data such as the user's blood pressure, heart rate, and body temperature. The health data acquisition unit may also collect data such as the user's amount of exercise, diet, and sleep time. Furthermore, the health data acquisition unit may evaluate the user's health condition based on the user's health checkup results and medical records. This enables suggestions based on the user's health condition.

[0051] The recommendation system may further include a social media acquisition unit that collects social media data of the user. The social media acquisition unit collects and analyzes information shared by the user on social media. For example, the social media acquisition unit may collect photos and comments posted by the user on social media. The social media acquisition unit may also collect data on accounts the user follows and groups the user joins. Furthermore, the social media acquisition unit may analyze the user's social media activity history and make suggestions based on the user's interests. This enables suggestions based on the user's social media activities.

[0052] The suggestion system may further include a location information acquisition unit that collects geographical location information of the user. The location information acquisition unit collects and analyzes the user's current geographical location information. For example, if the user is in a specific area, suggestions related to that area may be made. Furthermore, if the user is traveling, the location information acquisition unit may make suggestions related to the user's travel destination. Furthermore, if the user is at home, the location information acquisition unit may make suggestions related to the user's home. This enables suggestions based on the user's geographical location information.

[0053] The proposal system can further include a proposal history analysis unit that analyzes the user's past proposal history. The proposal history analysis unit collects and analyzes data on proposals the user has received in the past. For example, the proposal history analysis unit can analyze the content and evaluations of proposals the user has received in the past to understand the user's preferences and tendencies. The proposal history analysis unit can also extract characteristics of items that the user has been satisfied with from proposals he or she has received in the past and reflect these in the next proposal. Furthermore, the proposal history analysis unit can analyze characteristics of items that the user has been dissatisfied with from proposals he or she has received in the past and avoid similar proposals. This enables more accurate proposals to be made based on the user's past proposal history.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The acquisition unit registers the user's data on the cloud. The user's data includes a face photo, body type information, orientation, chronic illnesses, etc. The acquisition unit allows the user to upload a face photo and input information such as height, weight, preferred style, and allergy information. The acquisition unit also encrypts and stores the user's data to ensure security. For example, an authentication process is performed when the data is registered to protect the user's data. Step 2: The analysis unit uses the generation AI to analyze the data collected by the acquisition unit. The generation AI determines the shape of the face from a photograph of the face and the body type from the body type information. It also takes into account data on orientation and chronic illnesses to suggest items that are best suited to the user. For example, it analyzes the contours of the face and suggests the best eyeglass frames for the user. It can also suggest the best clothing size and design for the user based on the body type information, and the best shoes for the user based on chronic illness data. Step 3: The suggestion unit suggests items that are best suited to the user based on the analysis results obtained by the analysis unit. The suggestion unit suggests items such as glasses that suit the shape of the face, clothes that suit the body type, and shoes that take into account chronic illnesses. The suggestion unit also displays the suggestions on the user's smartphone or computer. For example, it may send a notification to the user's smartphone to display the suggestions, and display the suggestions on the user's computer so that the user can check the suggested items. The processing in the suggestion unit may be performed with or without the use of a generation AI.

[0056] (Example 2) A recommendation system according to an embodiment of the present invention registers personal data on the cloud and analyzes it with a generation AI. The system then makes optimal recommendations for clothing, glasses, accessories, and shoes based on data such as face, body type, orientation, and chronic illnesses. In this recommendation system, users register their own face photos, body type information, orientation, chronic illnesses, and other data on the cloud. The generation AI then analyzes this data and recommends items that are optimal for the user. For example, the system can recommend glasses that fit their face shape, clothes that fit their body type, and shoes that take chronic illnesses into consideration. This system allows users to easily find the items that are best for them, improving shopping efficiency. First, a user registers their own data on the cloud. At this time, they input detailed data such as a face photo, body type information, orientation, and chronic illnesses. For example, they upload a face photo and enter their height, weight, preferred style, allergy information, and so on. Next, the generation AI analyzes the input data. The generation AI determines the face shape from the face photo and the body type from the body type information. Furthermore, the system considers the orientation and chronic illness data to recommend items that are optimal for the user. For example, the system can recommend glasses that fit their face shape, clothes that fit their body type, and shoes that take chronic illnesses into consideration. The generated suggestions are provided to the user. For example, the suggestions are displayed on the user's smartphone or computer. The user can review the suggested items and purchase them. This system allows users to easily find the items that best suit them, improving shopping efficiency. For example, by being recommended glasses that fit the shape of the face, users will no longer have to worry about choosing glasses. Also, by being recommended clothes that fit the body type, users can avoid the hassle of trying on clothes. Furthermore, by being recommended shoes that take into account chronic illnesses, users can enjoy fashion while maintaining their health. This allows the suggestion system to efficiently collect and analyze user data and make optimal suggestions.

[0057] The proposal system according to the embodiment includes an acquisition unit, an analysis unit, and a proposal unit. The acquisition unit registers user data on the cloud. The user data includes, but is not limited to, a face photo, body shape information, inclinations, and chronic illnesses. For example, the acquisition unit allows the user to upload a face photo and input information such as height, weight, preferred style, and allergy information. The acquisition unit can also encrypt and store the user data to ensure security. For example, the acquisition unit may perform an authentication process when registering data to protect the user data. The analysis unit uses a generation AI to analyze the data collected by the acquisition unit. For example, the generation AI may determine the face shape from the face photo and the body type from the body shape information. The generation AI may also suggest items that are optimal for the user, taking into account the inclinations and chronic illness data. For example, the generation AI may analyze the facial contours and suggest the optimal eyeglass frames for the user. The generation AI may also suggest the optimal clothing size and design for the user based on the body shape information. The generation AI may also suggest the optimal shoes for the user, taking into account chronic illness data. The suggestion unit suggests optimal items to the user based on the analysis results obtained by the analysis unit. The suggestion unit suggests, for example, glasses that fit the user's face shape, clothes that fit the user's body type, or shoes that take chronic illnesses into consideration. The suggestion unit can also display the suggestions on the user's smartphone or computer. For example, the suggestion unit sends a notification to the user's smartphone to display the suggestions. The suggestion unit can also display the suggestions on the user's computer so that the user can check the suggested items. This allows the suggestion system to efficiently collect and analyze user data and make optimal suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can display suggestions generated by the generation AI to the user.

[0058] The acquisition unit can register the user's facial photo or body shape information, orientation, and chronic illness data on the Cloud. For example, the acquisition unit allows the user to upload a facial photo and input information such as height, weight, preferred style, and allergy information. The acquisition unit can also encrypt and store the user's data to ensure security. For example, the acquisition unit can perform an authentication process when registering data to protect the user's data. This enables more accurate analysis and suggestions by registering detailed user data on the Cloud. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input a user's facial photo into the generation AI and have the generation AI analyze the facial photo.

[0059] The analysis unit can determine the face shape from a facial photograph and the body type from body type information. The analysis unit can determine the face shape from a facial photograph, for example, using a generation AI. For example, the generation AI can analyze the facial contours and suggest the most suitable eyeglass frames for the user. The analysis unit can also determine the body type from the body type information, for example, using the generation AI. For example, the generation AI can suggest the most suitable clothing size and design for the user based on the body type information. This makes it possible to perform analysis to suggest the most suitable items for the user based on the facial photograph and body type information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input a user's face photo into the generation AI and have the generation AI determine the face shape.

[0060] The analysis unit can suggest items to the user based on data on preferences or chronic illnesses. The analysis unit, for example, uses a generation AI to suggest items that are optimal for the user, taking into account data on preferences and chronic illnesses. For example, the generation AI can suggest optimal clothing and accessories based on the user's hobbies and preferences. The generation AI can also suggest shoes that take into account the user's chronic illnesses. This makes it possible to make suggestions that take into account the user's preferences and chronic illnesses. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's preference data into the generation AI and have the generation AI suggest optimal items.

[0061] The suggestion unit can suggest glasses that fit the shape of the face, clothes that fit the body type, or shoes that take chronic illnesses into consideration. The suggestion unit can suggest glasses that fit the shape of the face, for example, using a generation AI. For example, the generation AI can analyze the contours of the face and suggest the best eyeglass frames for the user. The suggestion unit can also suggest clothes that fit the body type, for example, using the generation AI. For example, the generation AI can suggest the best size and design of clothes for the user based on body type information. The suggestion unit can also suggest shoes that take chronic illnesses into consideration, for example, the generation AI can suggest the best shoes taking into account the user's chronic illness data. This improves shopping efficiency by suggesting the best items for the user. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can display the suggestions generated by the generation AI to the user.

[0062] The suggestion unit can display the suggested content on the user's smartphone or computer. For example, the suggestion unit sends a notification to the user's smartphone and displays the suggested content. The suggestion unit also displays the suggested content on the user's computer, allowing the user to check the suggested items. This allows the user to check the suggested content and purchase them. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can display suggested content generated by a generation AI to the user.

[0063] The acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit can immediately acquire data, providing a smooth registration experience. Furthermore, if the user is feeling stressed, the acquisition unit can temporarily delay data acquisition and wait until the user calms down. Furthermore, if the user is in a hurry, the acquisition unit can quickly acquire data and complete registration in the shortest time possible. This allows for a smooth registration experience by adjusting the timing of data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0064] The acquisition unit can analyze the user's past data registration history and select the optimal acquisition method. For example, the acquisition unit can prioritize and suggest data acquisition methods (e.g., voice input or manual input) that the user has frequently used in the past. The acquisition unit can also automatically select necessary data items based on the types of data the user has previously registered. Furthermore, the acquisition unit can also suggest the most efficient data acquisition method from the user's past data registration history. This enables efficient data acquisition by selecting the optimal data acquisition method based on the past data registration history. Some or all of the above-mentioned processing in the acquisition unit can be performed, for example, using a generation AI or without using a generation AI. For example, the acquisition unit can input the user's past data registration history into the generation AI and have the generation AI select the optimal acquisition method.

[0065] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring data. The acquisition unit selects an appropriate data acquisition timing based on, for example, the user's current living situation (e.g., whether the user is at work or on vacation). The acquisition unit can also prioritize acquisition of related data items based on the user's areas of interest (e.g., fashion or health). Furthermore, the acquisition unit can filter unnecessary data items based on the user's living situation and areas of interest to perform efficient data acquisition. This enables efficient data acquisition based on the user's living situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0066] The acquisition unit can estimate the user's emotions and determine the priority of data to be acquired based on the estimated user's emotions. For example, when the user is relaxed, the acquisition unit can prioritize acquiring detailed data items. Furthermore, when the user is stressed, the acquisition unit can prioritize acquiring only basic data items. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring the most important data items. This enables efficient data acquisition by determining data priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the data priorities.

[0067] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring data related to that area. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring data related to the travel destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring data related to the user's home. This allows highly relevant data to be acquired efficiently by taking the user's geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, or without, the generation AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant data.

[0068] The acquisition unit can analyze the user's social media activities and acquire related data when acquiring data. The acquisition unit can acquire related data based on, for example, information shared by the user on social media. The acquisition unit can also analyze the user's social media activity history and acquire data based on their interests. Furthermore, the acquisition unit can acquire related data based on information about accounts the user follows on social media. This allows for efficient acquisition of related data based on the user's social media activities. Some or all of the above-described processing in the acquisition unit can be performed using, or without, a generation AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related data.

[0069] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise and concise analysis results when the user is stressed. Furthermore, the analysis unit can provide analysis results in a format that is easy to understand when the user is in a hurry. This allows for easy-to-understand analysis results to be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation of the analysis.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0071] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a face recognition algorithm to face photo data. The analysis unit can also apply a body type analysis algorithm to body type information. Furthermore, the analysis unit can select an appropriate algorithm for data on orientation and chronic illnesses and perform analysis. This enables highly accurate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0072] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise and concise analysis results when the user is stressed. Furthermore, the analysis unit can provide analysis results in a format that is easy to understand when the user is in a hurry. By adjusting the length of the analysis according to the user's emotions, it is possible to provide easy-to-understand analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0073] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of data submission into the generation AI and have the generation AI determine the priority of analysis.

[0074] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of analysis.

[0075] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions. When the user is stressed, the suggestion unit can also provide concise and to-the-point suggestions. When the user is in a hurry, the suggestion unit can also provide suggestions in a format that is quickly understandable. This allows the suggestion unit to adjust the way the suggestions are expressed based on the user's emotions, thereby providing easy-to-understand suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0076] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the item when making a suggestion. For example, the suggestion unit makes a detailed suggestion for an item with a high level of importance. The suggestion unit can also make a concise suggestion for an item with a low level of importance. Furthermore, the suggestion unit can adjust the depth and scope of the suggestion based on the importance of the item. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the item. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the importance of the item to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0077] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the item. For example, for clothes, the suggestion unit can apply a suggestion algorithm based on fashion style. For eyeglasses, the suggestion unit can also apply a suggestion algorithm based on face shape. For shoes, the suggestion unit can also apply a suggestion algorithm based on chronic illnesses or foot shape. This enables highly accurate suggestions by applying a suggestion algorithm depending on the category of the item. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the category of the item into the generation AI and cause the generation AI to apply an appropriate suggestion algorithm.

[0078] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions. When the user is stressed, the suggestion unit can also provide concise and to-the-point suggestions. When the user is in a hurry, the suggestion unit can also provide suggestions in a format that can be quickly understood. This allows the suggestion unit to adjust the length of the suggestions according to the user's emotions, thereby providing easy-to-understand suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0079] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the item. For example, the proposal unit prioritizes the most recent item. The proposal unit can also postpone the proposal of an item that was submitted earlier. Furthermore, the proposal unit can adjust the order of proposals based on the submission time. This enables efficient proposals by determining the priority of proposals based on the submission time of the item. Some or all of the above-described processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the submission time of the item into the generation AI and have the generation AI determine the priority of the proposals.

[0080] The suggestion unit can adjust the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant items. The suggestion unit can also postpone suggesting less relevant items. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the items. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of the items. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the relevance of items to the generation AI and cause the generation AI to adjust the order of suggestions. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can register user data on the Cloud using the control unit 46A of the smart device 14. The analysis unit can analyze the data using a generation AI using the specific processing unit 290 of the data processing device 12. The suggestion unit can suggest optimal items to the user using the control unit 46A of the smart device 14. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12, and can generate suggestion content using a generation AI and display it on the user's smartphone or PC. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can register user data on the Cloud using the control unit 46A of the smart glasses 214. The analysis unit can analyze data using a generation AI using the specific processing unit 290 of the data processing device 12. The suggestion unit can suggest optimal items for the user using the control unit 46A of the smart glasses 214. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12, and can generate suggestion content using a generation AI and display it on the user's smartphone or PC. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit can register user data on the Cloud by the control unit 46A of the headset type terminal 314. The analysis unit can analyze the data using a generation AI by the specific processing unit 290 of the data processing device 12, for example. The suggestion unit can suggest optimal items to the user by the control unit 46A of the headset type terminal 314, for example. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12, and can generate suggestion content using a generation AI and display it on the user's smartphone or PC. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can register user data on the Cloud by the control unit 46A of the robot 414. The analysis unit can analyze the data using a generation AI by the specific processing unit 290 of the data processing device 12, for example. The suggestion unit can suggest the most suitable item for the user by the control unit 46A of the robot 414, for example. The suggestion unit can also be realized by the specific processing unit 290 of the data processing device 12, and can generate suggestion content using a generation AI and display it on the user's smartphone or computer.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The recommendation system can further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit collects and analyzes data on items purchased in the past by the user. For example, the purchase history analysis unit can analyze the styles and brands of clothes and accessories purchased by the user in the past to understand the user's preferences. The purchase history analysis unit can also understand trends in items that the user frequently purchases and reflect this in the next recommendation. Furthermore, the purchase history analysis unit can analyze ratings and reviews of items purchased by the user in the past to extract characteristics of items that the user is satisfied with. This enables more accurate recommendations based on the user's purchase history.

[0083] The recommendation system may further include a lifestyle acquisition unit that collects lifestyle data of the user. The lifestyle acquisition unit collects and analyzes data related to the user's daily life. For example, the lifestyle acquisition unit may collect data on the user's exercise habits, diet, sleep patterns, etc. The lifestyle acquisition unit may also collect data on the user's hobbies and interests. The lifestyle acquisition unit may also collect data on the user's living environment and work situation. This makes it possible to make recommendations based on the user's lifestyle.

[0084] The suggestion system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the suggestion content based on the estimated user emotion. The emotion adjustment unit estimates the user's emotion in real time and adjusts the suggestion content. For example, if the user is relaxed, detailed suggestion content can be provided. Also, if the user is stressed, concise suggestion content can be provided that focuses on the main points. Furthermore, if the user is in a hurry, suggestion content can be provided in a format that can be quickly understood. In this way, by adjusting the suggestion content according to the user's emotion, it is possible to provide suggestions that are easy to understand.

[0085] The proposal system may further include a health data acquisition unit that collects health data of the user. The health data acquisition unit collects and analyzes data related to the user's health condition. For example, the health data acquisition unit may collect data such as the user's blood pressure, heart rate, and body temperature. The health data acquisition unit may also collect data such as the user's amount of exercise, diet, and sleep time. Furthermore, the health data acquisition unit may evaluate the user's health condition based on the user's health checkup results and medical records. This enables suggestions based on the user's health condition.

[0086] The suggestion system may further include an emotion timing adjustment unit that estimates the user's emotion and adjusts the timing of suggestions based on the estimated user emotion. The emotion timing adjustment unit estimates the user's emotion in real time and adjusts the timing of suggestions. For example, if the user is relaxed, suggestions can be made immediately. Also, if the user is feeling stressed, suggestions can be temporarily delayed. Furthermore, if the user is in a hurry, suggestions can be made quickly. This allows for smooth suggestions to be made by adjusting the timing of suggestions according to the user's emotion.

[0087] The recommendation system may further include a social media acquisition unit that collects social media data of the user. The social media acquisition unit collects and analyzes information shared by the user on social media. For example, the social media acquisition unit may collect photos and comments posted by the user on social media. The social media acquisition unit may also collect data on accounts the user follows and groups the user joins. Furthermore, the social media acquisition unit may analyze the user's social media activity history and make suggestions based on the user's interests. This enables suggestions based on the user's social media activities.

[0088] The suggestion system may further include an emotion priority determination unit that estimates the user's emotion and determines the priority of suggestions based on the estimated user's emotion. The emotion priority determination unit estimates the user's emotion in real time and determines the priority of suggestions. For example, if the user is relaxed, detailed suggestions can be given priority. Also, if the user is stressed, basic suggestions can be given priority. Furthermore, if the user is in a hurry, the most important suggestions can be given priority. In this way, efficient suggestions can be made by determining the priority of suggestions according to the user's emotion.

[0089] The suggestion system may further include a location information acquisition unit that collects geographical location information of the user. The location information acquisition unit collects and analyzes the user's current geographical location information. For example, if the user is in a specific area, suggestions related to that area may be made. Furthermore, if the user is traveling, the location information acquisition unit may make suggestions related to the user's travel destination. Furthermore, if the user is at home, the location information acquisition unit may make suggestions related to the user's home. This enables suggestions based on the user's geographical location information.

[0090] The suggestion system may further include an emotional expression adjustment unit that estimates the user's emotion and adjusts the way in which suggestions are expressed based on the estimated user emotion. The emotional expression adjustment unit estimates the user's emotion in real time and adjusts the way in which suggestions are expressed. For example, if the user is relaxed, detailed suggestions can be provided. If the user is stressed, concise suggestions that focus on the main points can be provided. Furthermore, if the user is in a hurry, suggestions can be provided in a format that can be quickly understood. In this way, by adjusting the way in which suggestions are expressed according to the user's emotion, suggestions that are easy to understand can be made.

[0091] The proposal system can further include a proposal history analysis unit that analyzes the user's past proposal history. The proposal history analysis unit collects and analyzes data on proposals the user has received in the past. For example, the proposal history analysis unit can analyze the content and evaluations of proposals the user has received in the past to understand the user's preferences and tendencies. The proposal history analysis unit can also extract characteristics of items that the user has been satisfied with from proposals he or she has received in the past and reflect these in the next proposal. Furthermore, the proposal history analysis unit can analyze characteristics of items that the user has been dissatisfied with from proposals he or she has received in the past and avoid similar proposals. This enables more accurate proposals to be made based on the user's past proposal history.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The acquisition unit registers the user's data on the cloud. The user's data includes a face photo, body type information, orientation, chronic illnesses, etc. The acquisition unit allows the user to upload a face photo and input information such as height, weight, preferred style, and allergy information. The acquisition unit also encrypts and stores the user's data to ensure security. For example, an authentication process is performed when the data is registered to protect the user's data. Step 2: The analysis unit uses the generation AI to analyze the data collected by the acquisition unit. The generation AI determines the shape of the face from a photograph of the face and the body type from the body type information. It also takes into account data on orientation and chronic illnesses to suggest items that are best suited to the user. For example, it analyzes the contours of the face and suggests the best eyeglass frames for the user. It can also suggest the best clothing size and design for the user based on the body type information, and the best shoes for the user based on chronic illness data. Step 3: The suggestion unit suggests items that are best suited to the user based on the analysis results obtained by the analysis unit. The suggestion unit suggests items such as glasses that suit the shape of the face, clothes that suit the body type, and shoes that take into account chronic illnesses. The suggestion unit also displays the suggestions on the user's smartphone or computer. For example, it may send a notification to the user's smartphone to display the suggestions, and display the suggestions on the user's computer so that the user can check the suggested items. The processing in the suggestion unit may be performed with or without the use of a generation AI.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that registers user data on the Cloud; an analysis unit that analyzes the data collected by the acquisition unit; a proposal unit that makes a proposal based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The acquisition unit The user's face photo, body shape information, orientation, and chronic illness data are registered on the cloud.

2. The system of claim 1.

3. The analysis unit Determine face shape from face photos and body type from body shape information 2. The system of claim 1.

4. The analysis unit Suggest items to users based on their orientation or medical condition data 2. The system of claim 1.

5. The proposal unit Suggest glasses that suit your face shape, clothes that suit your body type, and shoes that take chronic illnesses into consideration 2. The system of claim 1.

6. The proposal unit Display the suggestions on the user's smartphone or computer 2. The system of claim 1.

7. The acquisition unit Estimate user emotions and adjust data acquisition timing based on the estimated user emotions 2. The system of claim 1.

8. The acquisition unit Analyze the user's past data registration history and select the optimal acquisition method 2. The system of claim 1.

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